Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary SphereExtent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaVisible Scale: 1:144,000 to 1:1,000Number of Features: 36,569,286Source: USDA Natural Resources Conservation ServicePublication Date: December 2021Data from the gSSURGO database was used to create this layer.AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some mapunits have a null value for soil order, a custom script was used to populate this field using the Component Name (compname) and Mapunit Name (muname) fields. This field was created using the dominant soil order of each mapunit.Esri SymbologyHorizon TableEach map unit polygon has one or more components and each component has one or more layers known as horizons. To incorporate this field from the Horizon table into the attributes for this layer, a custom script was used to first calculate the mean value weighted by thickness of the horizon for each component and then a mean value of components weighted by the Component Percentage Representative Value field for each map unit. K-Factor Rock FreeEsri Soil OrderThese fields were calculated from the Component table using a model that included the Pivot Table Tool, the Summarize Tool and a custom script. The first 11 fields provide the sum of Component Percentage Representative Value for each soil order for each map unit. The Soil Order Dominant Condition field was calculated by selecting the highest value in the preceding 11 soil order fields. In the case of tied values the component with the lowest average slope value (slope_r) was selected. If both soil order and slope were tied the first value in the table was selected.Percent AlfisolsPercent AndisolsPercent AridisolsPercent EntisolsPercent GelisolsPercent HistosolsPercent InceptisolsPercent MollisolsPercent SpodosolsPercent UltisolsPercent VertisolsSoil Order - Dominant ConditionEsri Popup StringThis field contains a text string calculated by Esri that is used to create a basic pop-up using some of the more popular SSURGO attributes.Map Unit KeyThe Mapunit key field is found

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: Soil Survey Geographic Database (SSURGO) DownloaderItem Type: Web Mapping Application URLSummary: Download ready-to-use project packages with over 170 attributes derived from the SSURGO (Soil Survey Geographic Database) dataset.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: https://nmcdc.maps.arcgis.com/home/item.html?id=cdc49bd63ea54dd2977f3f2853e07fff link to Esri web mapping applicationFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=305ef916da574a71877edb15c3f47f08#overviewUID: 26Data Requested: Ag CensusMethod of Acquisition: Esri web mapDate Acquired: 6/16/22Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDINGDOCUMENTATION FROM DATA SOURCE URL: This application provides quick access to ready-to-use project packages filled with useful soil data derived from the SSURGO dataset.To use this application, navigate to your study area and click the map. A pop-up window will open. Click download and the project package will be copied to your computer. Double click the downloaded package to open it in ArcGIS Pro. Alt + click on the layer in the table of contents to zoom to the subbasin.Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryThe map packages were created from the October 2021 SSURGO snapshot. The dataset covers the 48 contiguous United States plus Hawaii and portions of Alaska. Map packages are available for Puerto Rico and the US Virgin Islands. A project package for US Island Territories and associated states of the Pacific Ocean can be downloaded by clicking one of the included areas in the map. The Pacific Project Package includes: Guam, the Marshall Islands, the Northern Marianas Islands, Palau, the Federated States of Micronesia, and American Samoa.Not all areas within SSURGO have completed soil surveys and many attributes have areas with no data. The soil data in the packages is also available as a feature layer in the ArcGIS Living Atlas of the World.AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Map Unit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Map Unit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some map units have a null value for soil order, a

This layer was developed by the Natural Resources Department of the Atlanta Regional Commission. The dataset contains polygonal hydrographic features including lakes, ponds, reservoirs, swamps, and marshes in the Metropolitan North Georgia Water Planning District.Original data were captured from the NHDWaterbody geospatial data layer included in the High Resolution National Hydrography Dataset Plus (NHDPlus HR). Features in the NHDWaterbody geospatial layer that intersected the Georgia State boundary were selected and spatially joined to Georgia county boundaries and the WBDHU8 geospatial data layer found in the U.S. Geological Survey's Watershed Boundary Dataset. Layers were spatially joined using the Largest Overlap matching method. The spatial join was removed upon calculating values for the COUNTY_FIPS, COUNTY_NAME, HUC8_ID, and HUC8_SUBBASIN attributes. The CLASS attribute was created to identify Lakes equal to or larger than 10 acres as Major and less than 0.5 acres as Minor. Data in the HYDRO_CAT and RESERVOIR_TYPE attributes were sourced from values encoded in the Feature Code (FCode) field of the NHDWaterbody geospatial data layer.Attributes:FEATURE = Type of hydrologic featureCLASS = Class used to identify major and minor waterbodiesGNIS_ID = A permanent, unique number assigned by the Geographic Names Information System (GNIS) to a geographic feature name for the sole purpose of uniquely identifying that name application as a record in any information system database, dataset, file, or documentGNIS_NAME = The Geographic Names Information System (GNIS) assigned proper name, specific term, or expression by which a particular geographic entity is known.HUC8_ID = 8-digit hydrologic unit code used to identify subbasins in the hydrologic unit systemHUC8_SUBBASIN = Subbasin name of the 8-digit hydrologic unit code in the hydrologic unit systemCOUNTY_FIPS = County Federal Information Processing System (FIPS) codeCOUNTY_NAME = County nameHYDRO_CAT = Hydrographic feature categoryRESERVOIR_TYPE = Type of reservoirACRES = Area of the feature in acresELEVATION = The vertical distance from a given datumGlobalID = A type of UUID (Universal Unique Identifier) in which values are automatically assigned by the geodatabase when a row is createdlast_edited_user = User to last edit featurelast_edited_date = Date feature was last editedShape = Feature geometryShape_Length = Length of the feature, which may differ from the field measured length due to differences in calculation. Units are map units.Shape_Area = Area of feature in map units squaredSource: U.S. Geological Survey, National Geospatial ProgramDate: 2023

This layer was developed by the Natural Resources Department of the Atlanta Regional Commission. The dataset contains polygonal hydrographic features including lakes, ponds, reservoirs, swamps, and marshes. Original data were captured from the NHDWaterbody geospatial data layer included in the High Resolution National Hydrography Dataset Plus (NHDPlus HR). Features in the NHDWaterbody geospatial layer that intersected the Georgia State boundary were selected and spatially joined to Georgia county boundaries and the WBDHU8 geospatial data layer found in the U.S. Geological Survey's Watershed Boundary Dataset. Layers were spatially joined using the Largest Overlap matching method. The spatial join was removed upon calculating values for the COUNTY_FIPS, COUNTY_NAME, HUC8_ID, and HUC8_SUBBASIN attributes. The CLASS attribute was created to identify Lakes equal to or larger than 10 acres as Major and less than 0.5 acres as Minor. Data in the HYDRO_CAT and RESERVOIR_TYPE attributes were sourced from values encoded in the Feature Code (FCode) field of the NHDWaterbody geospatial data layer.Attributes:FEATURE = Type of hydrologic featureCLASS = Class used to identify major and minor waterbodiesGNIS_ID = A permanent, unique number assigned by the Geographic Names Information System (GNIS) to a geographic feature name for the sole purpose of uniquely identifying that name application as a record in any information system database, dataset, file, or documentGNIS_NAME = The Geographic Names Information System (GNIS) assigned proper name, specific term, or expression by which a particular geographic entity is known.HUC8_ID = 8-digit hydrologic unit code used to identify subbasins in the hydrologic unit systemHUC8_SUBBASIN = Subbasin name of the 8-digit hydrologic unit code in the hydrologic unit systemCOUNTY_FIPS = County Federal Information Processing System (FIPS) codeCOUNTY_NAME = County nameHYDRO_CAT = Hydrographic feature categoryRESERVOIR_TYPE = Type of reservoirACRES = Area of the feature in acresELEVATION = The vertical distance from a given datumGlobalID = A type of UUID (Universal Unique Identifier) in which values are automatically assigned by the geodatabase when a row is createdlast_edited_user = User to last edit featurelast_edited_date = Date feature was last editedShape = Feature geometryShape_Length = Length of the feature, which may differ from the field measured length due to differences in calculation. Units are map units.Shape_Area = Area of feature in map units squaredSource: U.S. Geological Survey, National Geospatial ProgramDate: 2023

The Digital Surficial Units of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina consists of surficial units mapped as area (polygon) features. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). The data were captured, grouped and attributed as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.3.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The data layer is available as a feature class in a 9.1 personal geodatabase (grsm_geology.mdb). Attributed surficial contact lines that define the surficial unit polygons are present within the Surficial Contacts (GRSMSURA) data layer. The Surficial Units (GRSMSUR) GIS data layer is also available as a coverage export (.E00) file (GRSMSUR.E00), and as a shapefile (.SHP) file (GRSMSUR.SHP). Each GIS data format has an ArcGIS 9.1 layer (.LYR) file (GRSMSUR_GDB.LYR (geodatabase feature class), GRSMSUR_COV.LYR (coverage), GRSMSUR_SHP.LYR (shapefile) with map symbology that is included with the GIS data. See the Distribution Information section for additional information on data acquisition. The GIS data projection is NAD83, UTM Zone 17N. The data is within the area of interest of Great Smoky Mountains National Park.

This layer contains the fire perimeters from the previous calendar year, and those dating back to 1878, for California. Perimeters are sourced from the Fire and Resource Assessment Program (FRAP) and are updated shortly after the end of each calendar year. Information below is from the FRAP web site. There is also a tile cache version of this layer.About the Perimeters in this LayerInitially CAL FIRE and the USDA Forest Service jointly developed a fire perimeter GIS layer for public and private lands throughout California. The data covered the period 1950 to 2001 and included USFS wildland fires 10 acres and greater, and CAL FIRE fires 300 acres and greater. BLM and NPS joined the effort in 2002, collecting fires 10 acres and greater. Also in 2002, CAL FIRE’s criteria expanded to include timber fires 10 acres and greater in size, brush fires 50 acres and greater in size, grass fires 300 acres and greater in size, wildland fires destroying three or more structures, and wildland fires causing $300,000 or more in damage. As of 2014, the monetary requirement was dropped and the damage requirement is 3 or more habitable structures or commercial structures.In 1989, CAL FIRE units were requested to fill in gaps in their fire perimeter data as part of the California Fire Plan. FRAP provided each unit with a preliminary map of 1950-89 fire perimeters. Unit personnel also verified the pre-1989 perimeter maps to determine if any fires were missing or should be re-mapped. Each CAL FIRE Unit then generated a list of 300+ acre fires that started since 1989 using the CAL FIRE Emergency Activity Reporting System (EARS). The CAL FIRE personnel used this list to gather post-1989 perimeter maps for digitizing. The final product is a statewide GIS layer spanning the period 1950-1999.CAL FIRE has completed inventory for the majority of its historical perimeters back to 1950. BLM fire perimeters are complete from 2002 to the present. The USFS has submitted records as far back as 1878. The NPS records date to 1921.About the ProgramFRAP compiles fire perimeters and has established an on-going fire perimeter data capture process. CAL FIRE, the United States Forest Service Region 5, the Bureau of Land Management, and the National Park Service jointly develop the fire perimeter GIS layer for public and private lands throughout California at the end of the calendar year. Upon release, the data is current as of the last calendar year.The fire perimeter database represents the most complete digital record of fire perimeters in California. However it is still incomplete in many respects. Fire perimeter database users must exercise caution to avoid inaccurate or erroneous conclusions. For more information on potential errors and their source please review the methodology section of these pages.The fire perimeters database is an Esri ArcGIS file geodatabase with three data layers (feature classes):A layer depicting wildfire perimeters from contributing agencies current as of the previous fire year;A layer depicting prescribed fires supplied from contributing agencies current as of the previous fire year;A layer representing non-prescribed fire fuel reduction projects that were initially included in the database. Fuels reduction projects that are non prescribed fire are no longer included.All three are available in this layer. Additionally, you can find related web maps, view layers set up for individual years or decades, and tile layers here.Recommended Uses There are many uses for fire perimeter data. For example, it is used on incidents to locate recently burned areas that may affect fire behavior (see map left).Other uses include:Improving fire prevention, suppression, and initial attack success.Reduce and track hazards and risks in urban interface areas.Provide information for fire ecology studies for example studying fire effects on vegetation over time. Download the Fire Perimeter GIS data hereDownload a statewide map of Fire Perimeters hereSource: Fire and Resource Assessment Program (FRAP)

WBDHU6: This geospatial dataset represents the 3rd level (6-digit) hydrologic unit boundaries of the Watershed Boundary Dataset (WBD) layer for Washington. It was created by dissolving boundaries from the finer resolution hydrologic units to create these broader boundaries. See metadata for the wbd_wa_poly feature class for a more complete description of the WBD. USGS Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD) located here: http://pubs.usgs.gov/tm/11/a3/pdf/tm11-a3.pdf

This layer was developed by the Natural Resources Department of the Atlanta Regional Commission. The dataset contains polygonal hydrographic features including lakes, ponds, reservoirs, swamps, and marshes in the Atlanta Region.Original data were captured from the NHDWaterbody geospatial data layer included in the High Resolution National Hydrography Dataset Plus (NHDPlus HR). Features in the NHDWaterbody geospatial layer that intersected the Georgia State boundary were selected and spatially joined to Georgia county boundaries and the WBDHU8 geospatial data layer found in the U.S. Geological Survey's Watershed Boundary Dataset. Layers were spatially joined using the Largest Overlap matching method. The spatial join was removed upon calculating values for the COUNTY_FIPS, COUNTY_NAME, HUC8_ID, and HUC8_SUBBASIN attributes. The CLASS attribute was created to identify Lakes equal to or larger than 10 acres as Major and less than 0.5 acres as Minor. Data in the HYDRO_CAT and RESERVOIR_TYPE attributes were sourced from values encoded in the Feature Code (FCode) field of the NHDWaterbody geospatial data layer.Attributes:FEATURE = Type of hydrologic featureCLASS = Class used to identify major and minor waterbodiesGNIS_ID = A permanent, unique number assigned by the Geographic Names Information System (GNIS) to a geographic feature name for the sole purpose of uniquely identifying that name application as a record in any information system database, dataset, file, or documentGNIS_NAME = The Geographic Names Information System (GNIS) assigned proper name, specific term, or expression by which a particular geographic entity is known.HUC8_ID = 8-digit hydrologic unit code used to identify subbasins in the hydrologic unit systemHUC8_SUBBASIN = Subbasin name of the 8-digit hydrologic unit code in the hydrologic unit systemCOUNTY_FIPS = County Federal Information Processing System (FIPS) codeCOUNTY_NAME = County nameHYDRO_CAT = Hydrographic feature categoryRESERVOIR_TYPE = Type of reservoirACRES = Area of the feature in acresELEVATION = The vertical distance from a given datumGlobalID = A type of UUID (Universal Unique Identifier) in which values are automatically assigned by the geodatabase when a row is createdlast_edited_user = User to last edit featurelast_edited_date = Date feature was last editedShape = Feature geometryShape_Length = Length of the feature, which may differ from the field measured length due to differences in calculation. Units are map units.Shape_Area = Area of feature in map units squaredSource: U.S. Geological Survey, National Geospatial ProgramDate: 2023

Abstract

This dataset was derived from the NGIS Geodatabase. You can find a link to the source dataset in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived. The NGIS dataset has been clipped to the Gloucester subregion.

The NGIS geodatabase was developed by lead water agencies from all States and Territories based on the NGIS data model, which was developed and maintained by the Bureau. Refer to the NGIS Data Dictionary and Schema Diagram (included) for further information on the NGIS data model.

The format for NGIS is an ESRI file geodatabase. ESRI ArcGIS Version 10 (or beyond) is needed to use NGIS.

The National Groundwater Information System (NGIS) is a geodatabase for storing nationally consistent groundwater data based on ESRI's ArcHydro for Groundwater data model. It focuses on bore and bore log (lithology, bore construction and hydrostratigraphy) data. It also includes georasters and geovolumes for selected areas.

The Bureau of Meteorology maintains the NGIS data model that standardises and spatially-enables groundwater data. The lead water agencies in each State/Territory (plus Water Corporation in Western Australia) export data from their corporate groundwater databases into State/Territory NGIS geodatabases. The Bureau of Meteorology maintains the NGIS data model and collates State/Territory geodatabases into the national geodatabase.

The data product extent is Geographic Australia (as defined by Acts Interpretation Act 1901). The product will be updated in December of each year following the receipt of updated State/Territory geodatabases at the end of September through the Water Regulations (Commonwealth legislation that requires agencies to deliver water data to the Bureau under the Water Act).

The NGIS contains the following datasets:

Bore - Point feature class that represents the location of a bore and associated attributes. Multiple independently screened bore pipes are regarded as a separate bore features.

Bore log - Hydrostratigraphy log. Table with strata classified into hydrogeological units along a borehole

Lithology log - Table with driller's or geologist's description of rock or sediment types along a borehole

Construction log - Table with bore construction information along a borehole (e.g. casing and screen)

Bore line - 3D line feature class that represents the hydrogeologic units along a borehole

Construction line - 3D line feature class that represents the construction information (e.g. casing and screen) along a borehole

Hydrogeologic unit - Table summarising hydrogeologic units and hydrogeologic complexes and their attributes. Includes both state/territory and National Aquifer Framework (NAF) terminology.

Dataset History

Bore locations in the _National_Groundwater_Information_System_v1.1_Sept2013 database that were spatially located within the Gloucester subregion boundary were extracted and filed in the BA_SYD/GLO project area.

Dataset Citation

Bioregional Assessment Programme (2013) Gloucester NGIS bores. Bioregional Assessment Derived Dataset. Viewed 31 May 2018, http://data.bioregionalassessments.gov.au/dataset/e14ca3f6-6a49-4bd2-95b9-3fef6420bf49.

Dataset Ancestors

• Derived From National Groundwater Information System (NGIS) v1.1

Both this geodatabase and its associated web map display the shoreline miles and water acres within the 88 ocean, coastal, and Great Lakes National Park units. The following feature classes are included: "WRD Shoreline Miles Update 2024": Existing shoreline products from federal, state, and NPS sources were visually assessed for each park unit and compared to reference imagery within ESRI ArcGIS Pro to determine the best available data. The resulting shoreline delineation for each park unit was reviewed by NPS park, regional, national, and/or Inventory and Monitoring Network staff, and manual adjustments were made as needed to accurately reflect the shoreline. "WRD Water Acres Update 2024": For park units located in marine settings, “water acres” refers to ocean, estuarine, and tidally influenced waters. For park units in the Great Lakes region, “water acres” refers to freshwater. For all park units, freshwater bodies such as lakes, ponds, and rivers that exist inland of the marine or Great Lake shoreline are excluded. "WRD Park Boundaries 2022": Data current as of December 2022, provided by NPS Land Resources Division (LRD) and accessible via NPS DataStore (with a few exceptions as noted within the attribute table).

The Digital Geologic Units of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina consists of geologic units mapped as area (polygon) features. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). The data were captured, grouped and attributed as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.3.1. (available at: https://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The data layer is available as a feature class in a 9.1 personal geodatabase (grsm_geology.mdb). Attributed geologic contact lines that define the geologic unit polygons are present within the Geologic Contacts (GRSMGLGA) data layer. The Geologic Units (GRSMGLG) GIS data layer is also available as a coverage export (.E00) file (GRSMGLG.E00), and as a shapefile (.SHP) file (GRSMGLG.SHP). Each GIS data format has an ArcGIS 9.1 layer (.LYR) file (GRSMGLG_GDB.LYR (geodatabase feature class), GRSMGLG_COV.LYR (coverage), GRSMGLG_SHP.LYR (shapefile) with map symbology that is included with the GIS data. See the Distribution Information section for additional information on data acquisition. The GIS data projection is NAD83, UTM Zone 17N. That data is within the area of interest of Great Smoky Mountains National Park. This dataset is just one component of the Digital Geologic Map of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina. The data layers (feature classes) that comprise the Digital Geologic Map of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina include: GRSMAML (Alteration and Metamorphic Lines), GRSMATD (Geologic Attitude and Observation Points), GRSMFLD (Folds), GRSMFLT (Faults), GRSMGLG (Geologic Units), GRSMGLGA (Geologic Contacts), GRSMGPT (Point Geologic Features), GRSMGSL (Geologic Sample Localities), GRSMMIN (Mine Point Features), GRSMSEC (Cross Section Lines), GRSMSUR (Surficial Geologic Units), GRSMSURA (Surficial Contacts) and GRSMSYM (Fault Symbology). There are three additional ancillary map components, the Geologic Unit Information (GRSMGLG1) Table, the Source Map Information (GRSMMAP) Table and the Map Help File (GRSM_GEOLOGY.HLP). Refer to the NPS GRE Geology-GIS Geodatabase Data Model v. 1.3.1 (available at: https://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm) for detailed data layer (feature class) and table specifications including attribute field parameters, definitions and domains, and implemented topology rules and relationship classes.The corresponding Integration of Resource Management Applications (IRMA) NPS Data Store reference is Great Smoky Mountains National Park Geology.

An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area. This data is intended for read-only use. These data were prepared to describe Forest Service administrative area boundaries. The purpose of the data is to provide display, identification, and analysis tools for determining current boundary information for Forest Service managers, GIS Specialists, and others. The Forest Service has multiple types of boundaries represented by different feature classes (layers): Administrative, Ownership and Proclaimed. 1) ADMINISTRATIVE boundaries (e.g. AdministrativeForest and RangerDistrict feature classes) encompass National Forest System lands managed by an administrative unit. These are dynamic layers that should not be considered "legal" boundaries as they are simply intended to identify the specific organizational units that administer areas. As lands are acquired and disposed, the administrative boundaries are adjusted to expand or shrink accordingly. Please note that ranger districts are sub units of National Forests. An administrative forest boundary can contain one or more Proclaimed National Forests, National Grasslands, Purchase Units, Research and Experimental Areas, Land Utilization Projects and various "Other" Areas. If needed, OWNERSHIP boundaries (e.g. BasicOwnership and SurfaceOwnership feature classes) should be reviewed along with these datasets to determine parcels that are federally managed within the administrative boundaries. 2) OWNERSHIP boundaries (e.g. BasicOwnership and SurfaceOwnership feature classes) represent parcels that are tied to legal transactions of ownership. These are parcels of Federal land managed by the USDA Forest Service. Please note that the BasicOwnership layer is simply a dissolved version of the SurfaceOwnership layer. 3) PROCLAIMED boundaries (e.g. ProclaimedForest and ProclaimedForest_Grassland) encompass areas of National Forest System land that is set aside and reserved from public domain by executive order or proclamation. Please note that the ProclaimedForest layer contains only proclaimed forests while ProclaimedForest_Grassland layer contains both proclaimed forests and proclaimed grasslands. For boundaries that reflect current National Forest System lands managed by an administrative unit, see the ADMINISTRATIVE boundaries (AdministrativeForest and RangerDistrict feature classes). For a visual comparison of the different kinds of USFS boundary datasets maintained by the USFS, see the Forest Service Boundary Comparison map at https://usfs.maps.arcgis.com/apps/CompareAnalysis/index.html?appid=fe7b9f56217949a291356f08cfccb119. USFS boundaries are often referenced in national datasets maintained by other federal agencies. Please note that variations may be found between USFS data and other boundary datasets due to differing update frequencies. PAD-US (Protected Areas Database of the United States), maintained by the U.S. Geological Survey, is a "best available" inventory of protected areas including data provided by managing agencies and organizations including the Forest Service. For more information see https://gapanalysis.usgs.gov/padus/data/metadata/. SMA (Surface Management Agency), maintained by the Bureau of Land Management, depicts Federal land for the United States and classifies this land by its active Federal surface managing agency. It uses data provided by the Forest Service and other agencies, combined with National Regional Offices collection efforts. For more information see https://landscape.blm.gov/geoportal/catalog/search/resource/details.page?uuid=%7B2A8B8906-7711-4AF7-9510-C6C7FD991177%7D.

Description for i03_DAU_county_cnty2018 is as follows:

Detailed Analysis Unit-(DAU) Convergence via County Boundary cnty18_1 for Cal-Fire, (See metadata for CAL-FIRE cnty18_1), State of California.

The existing DAU boundaries were aligned with cnty18_1 feature class.

Originally a collaboration by Department of Water Resources, Region Office personnel, Michael L. Serna, NRO, Jason Harbaugh - NCRO, Cynthia Moffett - SCRO and Robert Fastenau - SRO with the final merge of all data into a cohesive feature class to create i03_DAU_COUNTY_cnty24k09 alignment which has been updated to create i03_DAU_COUNTY_cnty18_1.

This version was derived from a preexisting “dau_v2_105, 27, i03_DAU_COUNTY_cnty24k09” Detailed Analysis Unit feature class's and aligned with Cal-Fire's 2018 boundary.

Manmade structures such as piers and breakers, small islands and coastal rocks have been removed from this version. Inlets waters are listed on the coast only.

These features are reachable by County\DAU. This allows the county boundaries, the DAU boundaries and the State of California Boundary to match Cal-Fire cnty18_1.

DAU Background

The first investigation of California's water resources began in 1873 when President Ulysses S. Grant commissioned an investigation by Colonel B. S. Alexander of the U.S. Army Corps of Engineers. The state followed with its own study in 1878 when the State Engineer's office was created and filled by William Hammond Hall. The concept of a statewide water development project was first raised in 1919 by Lt. Robert B. Marshall of the U.S. Geological Survey.

In 1931, State Engineer Edward Hyatt introduced a report identifying the facilities required and the economic means to accomplish a north-to-south water transfer. Called the "State Water Plan", the report took nine years to prepare. To implement the plan, the Legislature passed the Central Valley Act of 1933, which authorized the project. Due to lack of funds, the federal government took over the CVP as a public works project to provide jobs and its construction began in 1935.

In 1945, the California Legislature authorized an investigation of statewide water resources and in 1947, the California Legislature requested that an investigation be conducted of the water resources as well as present and future water needs for all hydrologic regions in the State. Accordingly, DWR and its predecessor agencies began to collect the urban and agricultural land use and water use data that serve as the basis for the computations of current and projected water uses.

The work, conducted by the Division of Water Resources (DWR’s predecessor) under the Department of Public Works, led to the publication of three important bulletins: Bulletin 1 (1951), "Water Resources of California," a collection of data on precipitation, unimpaired stream flows, flood flows and frequency, and water quality statewide; Bulletin 2 (1955), "Water Utilization and Requirements of California," estimates of water uses and forecasts of "ultimate" water needs; and Bulletin 3 (1957), "The California Water Plan," plans for full practical development of California’s water resources, both by local projects and a major State project to meet the State's ultimate needs. (See brief addendum below* “The Development of Boundaries for Hydrologic Studies for the Sacramento Valley Region”)

DWR subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR), corresponding to the State’s major drainage basins. The next levels of delineation are the Planning Areas (PA), which in turn are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so are the smallest study areas used by DWR.

The DAU/counties are used for estimating water demand by agricultural crops and other surfaces for water resources planning. Under current guidelines, each DAU/County has multiple crop and land-use categories. Many planning studies begin at the DAU or PA level, and the results are aggregated into hydrologic regions for presentation.

This wye pipes feature class represents current wastewater information connecting the sewer service to either side of the street in the City of Los Angeles. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most rigorous geographic information of the sanitary sewer system using a geometric network model, to ensure that its sewers reflect current ground conditions. The sanitary sewer system, pump plants, wyes, maintenance holes, and other structures represent the sewer infrastructure in the City of Los Angeles. Wye and sewer information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the wastewater Wye is entered into attributes. Principal attributes include:WYE_SUBTYPE: wye subtype is the principal field that describes various types of points as either Chimney, Chimney Riser, Offset Chimney Riser, Siphon, Special Case, Spur, Tap, Tee, Unclassified, Vertical Tee, Vertical Tee Riser, Wye, Wye Drawn as a Tap.For a complete list of attribute values, please refer to (TBA Wastewater data dictionary).Wastewater Wye pipes lines layer was created in geographical information systems (GIS) software to display the location of wastewater wye pipes. The wyes lines layer is a feature class in the LACityWastewaterData.gdb Geodatabase dataset. The layer consists of spatial data as a line feature class and attribute data for the features. The lines are entered manually based on wastewater sewer maps and BOE standard plans, and information about the lines is entered into attributes. The wye pipes lines features are sewer pipe connections for buildings. The features in the Wastewater connector wye points layer is a related structure connected with the wye pipe line. The WYE_ID field value is the unique ID. The WYE_ID field relates to the Sewer Permit tables. The annotation wye features are displayed on maps alongside features from the Wastewater Sewer Wye pipes lines layer. The wastewater wye pipes lines are inherited from a sewer spatial database originally created by the City's Wastewater program. The database was known as SIMMS, Sewer Inventory and Maintenance Management System. Wye pipe information should only be added to the Wastewater wye pipes layer if documentation exists, such as a wastewater map approved by the City Engineer. Sewers plans and specifications proposed under private development are reviewed and approved by by Bureau of Engineering. The Department of Public Works, Bureau of Engineering's, Brown Book (current as of 2010) outlines standard specifications for public works construction. For more information on sewer materials and structures, look at the Bureau of Engineering Manual, Part F, Sewer Design, F 400 Sewer Materials and Structures section, and a copy can be viewed at http://eng.lacity.org/techdocs/sewer-ma/f400.pdf.List of Fields:SPECIAL_STRUCT: This attribute is the basin number.TOP_: When a chimney is present, this is the depth at the top of the chimney.BOTTOM: When a chimney is present, this is the depth at the bottom of the chimney.PL_HUNDS: This value is the hundreds portion of the stationing at the property line.SHAPE: Feature geometry.USER_ID: The name of the user carrying out the edits of the wye pipes data.TYPE: This is the old wye status and is no longer referenced.REMARKS: This attribute contains additional comments regarding the wye line segment, such as a line through in all caps when lined out on wye maps.WYE_NO: This value is the number of the line segment for the wye structure located along the pipe segment. This is a 2 digit value. The number starts at 1 for the first wye connected to a pipe. The numbers increase sequentially with each wye being unique.WYE_ID: The value is a combination of PIPE_ID and WYE_NO fields, forming a unique number. This 19 digit value is a key attribute of the wye lines data layer. This field relates to the Permit tables.C_TENS: This value is the tens portion of the stationing at the curb line.C_HUNDS: This value is the hundreds portion of the stationing at the curb line.WYE_SUBTYPE: This value is the type of sewer connection. Values: • 2 - Tap. • 8 - Siphon. • 13 - Wye Drawn as a Tap. • 9 - Special Case. • 6 - Chimney riser. • 4 - Chimney. • 5 - Vertical Tee Riser. • 7 - Vertical tee. • 10 - Spur. • 11 - Unclassified. • 12 - Offset Chimney Riser. • 1 - Wye. • 3 - Tee.SIDE: The side of the pipe looking up stream to which structure attaches. Values: • U - Unknown. • L - Left. • R - Right. • C - Centered.ASSETID: User-defined unique feature number that is automatically generated.PL_DEPTH: This value is the depth of the service connection at the property line.DEPTH: This value is the depth of the Wye from the surface in feet.STAT_HUND: This value is the hundreds portion of the stationing.ENG_DIST: LA City Engineering District. The boundaries are displayed in the Engineering Districts index map. Values: • H - Harbor Engineering District. • C - Central Engineering District. • V - Valley Engineering District. • W - West LA Engineering District.PIPE_ID: The value is a combination of the values in the UP_STRUCT, DN_STRUCT, and PIPE_LABEL fields. This is the 17 digit identifier of each pipe segment and is a key attribute of the pipe line data layer. This field named PIPE_ID relates to the field in the Annotation Pipe and to the field named PIPE_ID in the Pipe line feature class data layers.OBJECTID: Internal feature number.ENABLED: Internal feature number.REHAB: This attribute indicates if the wye pipe has been rehabilitated.C_DEPTH: This value is the depth of the service connection at the curb line.STAT_TENS: This value is the tens portion of the stationing.BASIN: This attribute is the basin number.LAST_UPDATE: Date of last update of the point feature.STATUS: This value is the active or inactive status of the wye pipes. Values: • Capped - Capped. • INACTIVE - Inactive.PL_TENS: This value is the tens portion of the stationing at the property line.CRTN_DT: Creation date of the point feature.SERVICEID: User-defined unique feature number that is automatically generated.SHAPE_Length: Length of feature in internal units.

This dataset represents a water shortage social vulnerability analysis performed by DWR using Census 2021 block groups as the unit of analysis. This feature class includes water shortage social vulnerability indicators and scores from an analysis done by CA Department of Water Resources, joined to the 2021 Census Block Groups. Most of the indicators were pulled from the ACS (American Communities Survey). These indicators were joined to the block groups to represent a spatial analysis of the social vulnerability of communities to water shortages. The goal of this data is to provide a spatial representation of social and economic factors that can affect water shortage vulnerability in the state of California. Model indicators included in the attribute table are percent of the population 65 and older, percent of households with no vehicles, percent of population 25 and older without a high school diploma. Please note that all of these model indicators are estimated values pulled from the ACS (American Communities Survey). All model indicators are added together using sum-rank methods outlined in the Drought and Water Shortage Risk Scoring: California's Domestic Wells and State Smalls Systems document and CDC standards for sum-rank methods. This data is for the 2022 analysis using 2017-2021 ACS estimates and 2021 Census block groups. From the draft Drought and Water Shortage Vulnerability Scoring document: “Water Code Division 6 Part 2.55 Section 8 Chapter 10 (Assembly Bill 1668) effectively requires California Department of Water Resources (DWR), in consultation with other agencies and an advisory group, to identify small water suppliers and “rural communities” that are at risk of drought and water shortage. Following legislation passed in 2021 and signed by Governor Gavin Newsom, the Water Code Division 6, Section 10609.50 through 10609.80 (Senate Bill 552 of 2021) effectively requires the California Department of Water Resources to update the scoring and tool periodically in partnership with the State Water Board and other state agencies. This document describes the indicators, datasets, and methods used to construct this deliverable. " A spatial analysis was performed on the 2021 Census Block Groups, modified PLSS sections, and small water system service areas using a variety of input datasets related to drought vulnerability and water shortage risk. These indicator values were subsequently rescaled and summed for a final physical vulnerability score for the sections and small water system service areas. The 2021 Census Block Groups were joined with ACS data to represent the social vulnerability of communities, which is relevant to drought risk tolerance and resources. These three feature datasets contain the units of analysis (modified PLSS sections, block groups, small water systems service areas) with the model indicators for vulnerability in the attribute table. Model indicators are calculated for each unit of analysis according to the Vulnerability Scoring documents provided by Julia Ekstrom (Division of Regional Assistance). All three feature classes are DWR analysis zones that are based off existing GIS datasets. The spatial data for the sections feature class is extracted from the Well Completion Reports PLSS sections to be aligned with the work and analysis that SGMA is doing. These are not true PLSS sections, but a version of the projected section lines in areas where there are gaps in PLSS. The spatial data for the Census block group feature class is downloaded from the Census. ACS (American Communities Survey) data is joined by block group, and statistics calculated by DWR have been added to the attribute table. The spatial data for the small water systems feature class was extracted from the State Water Resources Control Board (SWRCB) SABL dataset, using a definition query to filter for active water systems with 3000 connections or less. None of these datasets are intended to be the authoritative datasets for representing PLSS sections, Census block groups, or water service areas. The spatial data of these feature classes is used as units of analysis for the spatial analysis performed by DWR. These datasets are intended to be authoritative datasets of the scoring tools required from DWR according to Senate Bill 552. Please refer to the Drought and Water Shortage Vulnerability Scoring: California's Domestic Wells and State Smalls Systems documentation for more information on indicators and scoring. These estimated indicator scores may sometimes be calculated in several different ways, or may have been calculated from data that has since be updated. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.4, dated September 14, 2022. DWR makes no warranties or guarantees — either expressed or implied— as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov.